Multi-scale fusion is widely adopted in many high-level vision tasks, such as detection [1]}, [2]}, [3]}, semantic segmentation [4]}, etc. On the one hand, these tasks involve both location and classification. They need multi-scale information to make a balance between these two sub-tasks. On the other hand, these tasks need to tackle objects of various sizes. They scale-invariant representations to get more stable performances. In these tasks, most methods [5]}, [1]}, [4]} firstly extract a feature pyramid, which contains feature maps of different spatial sizes, and then fuse feature maps to obtain multi-scale information. However, as we have discussed above, the fused features may be not well spatially-aligned. This non-alignment may hurt the accuracy of location. For detection and segmentation, this influence could be ignored, because their evaluation metrics (IOU) are less sensitive to the accuracy of location. While HPE methods are evaluated by OKS, which will be heavily influenced by pixel-level errors. Thus the non-alignment may restrict the performance of HPE methods. In the proposed adaptive dilated convolution, multi-scale features are of the same spatial sizes, which may be more friendly to human pose estimation.
